An inventory manages resources that may be used to respond to a customer request. A customer request may be satisfied using one resource or combining several resources. These resources may be provided to customers through several products sold at different prices. A revenue management system is able to update and manage parameters that limit the number of each product that can be sold.
It is known that passengers with low willingness-to-pay purchase air travel tickets earlier than the passengers willing to pay more, for products having the same resources. As a result the revenue management system must decide between selling a product immediately, and waiting for a booking request that brings more revenue later. In the former case, early selling of the resources means that they will unavailable in the future, when prices may be higher. Revenue management systems are typically used in the airline industry, the hospitality industry, car rental industry, etc. to maximize revenue coming from sales of products using the available resources, such as airplane seats, hotel rooms, rental cars, etc, The output from a revenue management system are controls, such as booking limits (the number of units of a given product that may be sold), protection levels (the number of resources that should be reserved for a given product only) or bid prices (also known as a threshold price at which a product may be sold if and only if its price is above the bid price). The present invention may be applied regardless of the output of the revenue management system to which it pertains.
In revenue management systems, the outputs (e.g., booking limits) are computed with the object of maximizing the revenue “to-go” (i.e., the revenue to be made from the sale of the resources that are still available), or the expected revenue to go if the stochastic nature of the demand (number of customers that request a product) is taken into account. An important input to the optimization is an estimate (also called forecast) of demand for every product. It is notoriously difficult to calculate accurate forecasts. Currently, forecasts they are often based on data recorded from previous sales for flights which have already departed. More pertinent data relating to the prediction of patterns of demand would mean that more accurate forecasting could be possible.
FIG. 1 shows a known revenue management system 100. The revenue management system manages an inventory of resources 102 based on historical data 104. The revenue management system further includes three modules: an unconstraining (UNC) module 106, a forecasting (FCT) module 108 and an optimization (OPT) module 110.
In some situations or markets, the historical data is unreliable as it may include factors which have changed, for example new routes, schedule changes, the accommodation of previously booked and reserved resources, changes in behaviour on different days or any other variation or change which may occur to the resource that may or may not have previously occurred. The reliance on historical data then results in a mismatch between the forecast and actual demand.
This in turn results in sub-optimal recommendations being calculated by the revenue management system. Thus some resources may be wasted (e.g., empty seats at the departure time of a flight) and as a result the demand wiii be diluted, meaning that some passengers willing to pay high fares have finally booked in lower booking classes (typically with lower fares). As a result, the actual revenue could be much lower than the revenue that could have been made if better information about demand had been available.
FIG. 2 is a graph of actual value of demand against actual revenue and shows a revenue distribution 200 and missed revenue 202.
Most revenue management models assume that demand can be expressed using a probability distribution. However, these probability distributions are difficult to estimate, especially for new or unstable markets. Investigations into the field of robust optimization to find new methods dedicated to revenue management with less well defined demands are ongoing. Robust optimization methods are intended to produce a satisfactory level of revenue in a wide range of possible actual values of demand: in other words, these methods offer robustness with respect to changes in input parameters, such as demand forecast. The problems associated with applying robust optimization to the field of revenue management are not resolved.
Recent papers (Lan Y., Gao H., Ball M. and Karaesmen I., “Revenue Management with Limited Demand Information”, Management Science, INFORMS, 54(3):1594-1609, 2008; and Perakis G. and Roels G., “Robust Controls for Network Revenue Management”, Manufacturing and Service Operations Management, INFORMS, Forthcoming) have disclosed nested booking limits for static model and dynamic policies. These papers introduce the criteria of robustness, which are then compared. The criteria of robustness may include absolute robustness or absolute deviation and on average appear to behave as well as baseline methods such as Expected Maximization Seat Revenue, which calculate the optimum revenue in a traditional sense. There is no mention of how the principles can be implemented in a revenue management system.